An Expert system for flexible manufacturing system scheduling : knowledge acquisition and development PublicDeposited

Descriptions

Expert systems have been suggested as a solution
for difficult problems, including FMS scheduling. As
one of the aspects of artificial intelligence (AI), expert
systems have achieved considerable success in recent
years in medical science, chemistry, and engineering.
However, building an expert system is a difficult
task, the most crucial problem being that of knowledge
acquisition. Obtaining expert knowledge is a difficult
and time-consuming process. Moreover, since FMSs represent
a relatively new technology, experts capable of
FMS planning and scheduling are generally unavailable.
One possible solution for this problem is to train
a non-expert operator, allow the operator to practice
with a simulated system and accumulate experience, and
then build an expert system using the newly acquired
knowledge. To this end, an interactive graphic simulation
method for the effective utilization of human
pattern-recognition ability is proposed. Once the
required knowledge is elicited through an interactive
graphic simulation model, an expert system is developed
from acquired rules. The method includes an FMS simulation
model, a Gantt chart-based schedule, a simulator,
an expert system, and a human operator. First, an
initial schedule is simulated, utilizing the expert
system to determine the loading sequence and a dispatching
rule. The schedule is then updated by an expert
system and/or human operator with the capability
of maximizing schedule objectives, while at the same
time saving reasons for changes as new production
rules, which are subsequently generalized and added to
the expert system knowledge base.
The system is implemented in Smalltalk/V on an IBM
PC/AT and the implementation is based upon a detailed
sample problem. It was determined that a human operator
can obtain near-optimum schedules in short time
periods, at the same time gaining valuable experience
in use of the scheduling process. Furthermore, it was
determined that this model can be a useful training
device for inexperienced operators and a time-saving
decision-making aid for expert schedulers.